code: def h2o_ts_pipeline_evaluation(visualization=False):
      train_data, test_data = get_ts_data(
          n_steps=500, forecast_length=3
      )
  
      pipeline = pipeline_h2o_ts()
      pipeline.fit(input_data=train_data)
      test_pred = pipeline.predict(input_data=test_data)
      # Calculate metric
      test_pred = np.ravel(np.array(test_pred.predict))
      test_target = np.ravel(np.array(test_data.target))
  
      plot_info = [
          {
              "idx": train_data.idx,
              "series": train_data.features,
              "label": "Actual time series",
          },
          {
              "idx": test_data.idx,
              "series": test_target,
              "label": "Test part",
          },
      ]
      metrics_info = {}
      rmse = mean_squared_error(test_target, test_pred, squared=False)
      mae = mean_absolute_error(test_target, test_pred)
  
      metrics_info["Metrics"] = {
          "RMSE": round(rmse, 3),
          "MAE": round(mae, 3),
      }
      if visualization:
          visualise(plot_info)
      print(metrics_info)
  
file_path: FEDOT/examples/advanced/automl/h2o_example.py
imports: import numpy
  from sklearn.metrics import roc_auc_score, mean_squared_error, mean_absolute_error
  from examples.advanced.time_series_forecasting.composing_pipelines import visualise
  from examples.simple.pipeline_import_export import create_correct_path
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.operation_types_repository import OperationTypesRepository
  from test.unit.tasks.test_classification import get_iris_data
  from test.unit.tasks.test_forecasting import get_ts_data
  from test.unit.tasks.test_regression import get_synthetic_regression_data, get_rmse_value
label_desc: Use image node_id 7 for calling a global function with example usage: h2o_ts_pipeline_evaluation(visualization) without return types